What happens when you ask senior Facility Management leaders in North America and Europe the same question about AI?
Earlier this year, IFMA created that opportunity through two executive conversations: the Executive Summit in Charlotte, North Carolina, and the Executive Roundtable in The Hague, Netherlands. Both groups explored the future of AI in FM through a similar set of questions, covering strategy, governance, workforce capability, ESG, sustainability, and the future AI native FM organization.
The value of comparing the two conversations is not that they produced identical answers. They did not. The value lies in what the differences reveal.
Together, they point to a bigger question for FM leaders everywhere: is the profession preparing for AI as a technology shift, or as a leadership transformation?
In North America, AI leadership was presented as a maturity journey. The dominant message was that FM leaders cannot move confidently into an AI enabled future without first building the conditions that make AI useful, trusted, and scalable. Across the discussion, four themes emerged: purpose, data, governance, and workforce readiness.
Purpose sat at the heart of that conversation. Participants were clear that AI should not be pursued because it is fashionable, or because the tools are now available. It needs to be connected to real FM pain points, business objectives, and measurable outcomes. AI needs to solve meaningful problems, not simply demonstrate technological novelty.
That focus on purpose led quickly to the question of data. The Charlotte discussion emphasized that AI depends on a reliable data backbone, which is a particular challenge for FM because operational data is often fragmented across buildings, systems, suppliers, service lines, dashboards, and informal knowledge. Without accessible and meaningful data, AI outputs may look sophisticated but remain difficult to trust or act on.
Governance was also central, but not as a barrier to innovation. Participants framed governance, cybersecurity, ethics, privacy, and trust as the conditions that allow AI to scale responsibly. The implication was clear: FM leaders need guardrails, but those guardrails should create confidence rather than close down experimentation.
The North America findings also pointed to a changing workforce and operating model. AI enabled FM will require confidence, curiosity, judgement, and the ability to interpret AI outputs critically. It will also require a stronger connection between AI, ESG, circularity, and business value.
Taken together, the Charlotte findings suggest that FM does not simply need AI. It needs the organizational maturity to use AI well.
In Europe, many of the same ideas surfaced, but with a distinct emphasis. The Hague participants repeatedly framed AI as a business transformation issue, not an IT project. The discussion repeatedly returned to four themes: work redesign, responsible experimentation, human capability, and new models of collaboration.
One of the clearest messages was that FM leaders should not bolt AI onto broken work. If they do, they may simply create “faster chaos.”
This makes work design central. The European data suggested that leaders need to understand roles, workflows, decisions, prompts, and accountability before scaling AI. AI should not simply automate the current model. It should help FM rethink the model.
The European discussion also placed strong emphasis on responsible momentum. Participants recognized the risks of AI, including cybersecurity, bias, over reliance on data, and business continuity. But they also warned against inertia. One of the most memorable messages was: “Don’t panic, but do not stand still.”
This is a powerful leadership insight. In AI adoption, caution is necessary, but paralysis is risky. The European findings suggest that leaders need the courage to experiment, supported by guardrails, psychological safety, and clear ownership.
Human capability was another defining feature of the European report. Participants emphasized social skills, human relations, purpose, inclusion, AI literacy, and change leadership. Rather than reducing the importance of people, AI appears to increase the importance of distinctly human capabilities.
The future organization was also imagined differently. In Europe, the discussion also introduced the idea of human, augmented, and agentic collaboration. FM’s future was not framed as human versus AI, but as a blended model in which humans, augmented workers, and AI agents share work in new ways.
When viewed together, these two reports provide a more complete picture of what it will take for facility management to succeed in an AI-enabled future. North America shows the foundations that FM needs to build: purpose, data, governance, capability, sustainability, and operating model readiness. Europe shows the conditions that make transformation responsible in practice: trust, culture, psychological safety, work redesign, human capability, and measurable value.
The similarity is important. In both regions, AI was not framed as a tool that can simply be purchased and deployed. It was framed as something that requires leadership, governance, human judgement, and organizational readiness.
The difference is equally important. The North America findings lean toward readiness and maturity. They ask whether FM has the structures, data, governance, and capability to use AI well. The European findings lean toward responsible transformation. They ask whether FM has the culture, courage, trust, and work design needed to make AI meaningful and human centered.
That distinction matters because global FM leaders will need to understand both the foundations that enable AI and the human conditions that make adoption responsible in practice.
The future of AI in Facility Management will not be defined by AI itself. It will be defined by the quality of leadership around it.
The conversations in North America and Europe offered two distinct perspectives on what that leadership may require. Together, they highlight the foundations, capabilities, and cultural conditions that organizations need to create meaningful value from AI.
This article only scratches the surface of the two executive conversations. The full reports go deeper into the participant insights, regional findings, thematic analysis, and practical implications for FM leaders.
They provide discussion points for leadership teams, questions to test organizational readiness, and practical ways to think about AI governance, workforce capability, ESG, data, work redesign, and the future AI native FM operating model.
For any FM leader trying to understand what AI means beyond the hype, the reports offer something more useful than prediction. They offer a grounded view from senior leaders grappling with the same questions in real time.
Read the full reports:
Editor's note: This article was written by IFMA’s Director of Knowledge and Insights, Dr. Matt Tucker, Ph.D. A Fulbright Scholar with more than 80 publications and over 2,500 citations, Matt’s research has become foundational to the profession — guiding strategy, policy and practice worldwide. His work spans scientific journals, industry reports, book chapters and conference papers, making him a sought-after voice on global FM stages.